This disclosure relates generally to diagnostic imaging and, more particularly, to an apparatus and method of reducing artifacts due to high-density objects in computed tomography (CT).
Typically, in computed tomography (CT) imaging systems, an x-ray source emits a fan or cone-shaped beam toward a subject or object, such as a patient or a piece of luggage. Hereinafter, the terms “subject” and “object” shall include anything capable of being imaged. The beam, after being attenuated by the subject, impinges upon an array of radiation detectors. The intensity of the attenuated beam radiation received at the detector array is typically dependent upon the attenuation of the x-ray beam by the subject. Each detector element of the detector array produces a separate electrical signal indicative of the attenuated beam received by each detector element. The electrical signals are transmitted to a data processing system for analysis which ultimately produces an image.
Generally, the x-ray source and the detector array are rotated about the gantry within an imaging plane and around the subject. X-ray sources typically include x-ray tubes, which emit the x-ray beam at a focal point. X-ray detectors typically include a collimator for collimating x-ray beams received at the detector, a scintillator for converting x-rays to light energy adjacent the collimator, and photodiodes for receiving the light energy from the adjacent scintillator and producing electrical signals therefrom. Typically, each scintillator of a scintillator array converts x-rays to light energy. Each scintillator discharges light energy to a photodiode adjacent thereto. Each photodiode detects the light energy and generates a corresponding electrical signal. The outputs of the photodiodes are transmitted to the data processing system for image reconstruction. Imaging data may be obtained using x-rays that are generated at a single polychromatic energy. However, some systems may obtain multi-energy images that provide additional information for generating images.
Objects with high x-ray absorption properties (e.g., metal) can cause artifacts in reconstructed CT images, often resulting in images having low or non-diagnostic image quality. For example, metal implants such as amalgam dental fillings, joint replacements (i.e., plates and/or pins used in hips, knees, shoulders, etc.), surgical clips, biopsy needles, or other hardware may generate streak or starburst artifacts in the formation of such images. Such artifacts typically result from a sharp difference in signal attenuation at the boundary of the metal implants and a patient's anatomy.
That is, high density objects such as metal present in the body of patients can cause severe artifacts that hinder a diagnosis. These artifacts may be caused due to several factors such as beam-hardening, scatter, photon starvation, partial volume, aliasing, and under-range in the data acquisition, as examples. The artifacts can be reduced by known metal artifact reduction (MAR) techniques. For instance, advanced beam-hardening, noise reduction, and scatter correction have been proposed for standard filtered back-projection algorithm. In another example, an iterative reconstruction algorithm for MAR incorporates the shape of the metal and may use a polyenergetic model to reduce beam-hardening artifacts. These algorithms belong to a category generally referred to as model-based algorithms.
In addition to the category of model-based algorithms, another class of algorithms can be described as “sinogram in-painting”. In this class of algorithms, the sinogram data that is corrupted by the high-density object is discarded and replaced by either data interpolated using neighboring projections or data estimated by solving a cost function using some iterative algorithms, or by a forward projection of a prior image. These methods can be single steps or can involve successive refinements via an iterative process.
The efficacy of a given MAR technique depends on the type and amount of metal, as well as the size of the patient. However, typically a MAR technique is implemented in CT systems by prompting a user, such as a scanning technician, to initiate a MAR correction by activating a switch prior to the reconstruction process. If the MAR correction is activated, then the algorithm is implemented using a technique that is independent of the type of metal. That is, typically a MAR algorithm is a user option that may be selected, or not, during reconstruction. As such, titanium implants in hips or extremities are examples of low Z materials that may be treated in the same fashion as high Z material implants such as dental fillings or stainless steel.
Thus, there is a need to improve implementation of MAR algorithms in CT scanners.
Further, known methods for MAR correction can include model-based algorithms or sinogram in-painting. In sinogram in-painting, sinogram data that is corrupted by the high-density object is discarded and may be replaced by data interpolated using neighboring projections or data estimated by solving a cost function using some iterative algorithms, or by a forward projection of a prior image.
Pure interpolation techniques, however, may create additional artifacts in the reconstructed image due to inconsistency in the data. Hence two-pass techniques are more favorable in terms of artifact reduction. The first step is correction using an interpolation technique and the resultant first pass image is then used to generate a prior image. During the second pass the corrupted data is replaced by the forward projection of the prior image to generate the in-painted sinogram.
In a typical CT acquisition a prior image is not available and hence may be generated using corrupted data. In an ideal sense, the prior image should include any knowledge of the object without the high-density artifacts. Availability of an atlas, and then registering the images to the atlas, can in principle lead to a prior image. In practice, the prior image is typically generated by performing a non-linear transformation on either the original image or a first pass MAR image. However, it is often challenging to use the original image in the presence of metal (or high density) artifacts, and a first-pass MAR image typically has degraded information content close to the metal, thus the first-pass MAR image is not consistent with the original image, and non-linear processing will not add content to the prior image.
In another approach, the in-painted sinogram data can be combined with the original data to generate projection data which can then be fed to a reconstruction algorithm. This approach is general enough and can be used in conjunction with in-painted data generated using any of the methods described above. However, known in-painting techniques generally do not take into account the severity of the metal or high-density artifact.
Therefore, it would be desirable to improve image reconstruction when high-density objects are present.